Process Expertise from Data and BI
We know the best practices an agent needs. Without these guardrails, it produces plausible-sounding nonsense.
AI Strategy & AI Consulting
AI Consulting From Strategy to Operations
You don’t need to convince anyone that AI matters anymore. The harder questions are where to start, what data you need, and how to turn a prototype into something that works reliably in day-to-day operations. Technology is rarely the issue.
b.telligent supports companies from AI strategy through to operations. Our approach: AI should lighten your team’s workload, not replace people. The final decision always rests with a human.
AI pressure hits every role differently. The symptoms are concrete: pilots that never go into production. A data warehouse that blocks every modernization effort. Model costs that no one can attribute to a team. Prompts that no one tests.
The challenges look different depending on your role:
You Lead a Company or Business Unit
CEO, CIO, CTO
- The pressure to use AI is high. A solid business case is missing.
- Every department pulls in a different direction.
- You want to avoid vendor lock-in. With a thousand options, you don’t know what to bet on.
You’re Responsible for Data
Head of Data, CDO, Head of AI
- The data warehouse is outdated, and the migration has been on hold for years.
- The platform isn’t AI-ready: metadata is missing, access is unclear.
- No one can see where AI costs come from.
You Build Platforms
Data Engineer, Data Scientist, Architect
- Prompt tinkering instead of reproducible pipelines.
- No guardrail layer, no tests, no benchmarking.
- Agentic AI sounds promising. How much autonomy is acceptable is still unclear.
Three roles, three perspectives, one shared bottleneck: as long as the data foundation can’t carry the load and the target architecture is undecided, every AI initiative remains an experiment.
Why b.telligent?
Good models aren’t enough. You need process knowledge.
An agent can only migrate a data warehouse effectively if someone understands the target architecture. b.telligent has been building data platforms and BI landscapes since 2004 and carried out migrations manually for years. The agents are new. The knowledge of what the end result has to look like is not.
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Our Technology Stack
We work vendor-independently and with certifications on the platforms that already run in your landscape. The choice is driven by your architecture, not by our portfolio.
Where We Come In
Six service areas, one principle: we start where your company stands today. Whether you need initial orientation, a first pilot in production, or the platform that makes both scalable.
AI Strategy & Roadmap
Assess potential, prioritize initiatives, and set a shared direction for your AI journey.
Agentic Migration
Modernize DWH, BI, and reporting landscapes with AI support, step by step and with quality assurance.
Agentic AI & Workflows
Support or automate recurring processes with orchestrated agents, with clear guardrails and graduated levels of autonomy.
Generative AI & Company Knowledge
Use GenAI securely for knowledge, content, and processes, and make company knowledge available where it’s needed through RAG applications.
AI Platforms & Data Engineering
Build a scalable foundation for models, applications, governance, and cost control, and develop data solutions faster with AI support. Implement first use cases iteratively and create value right away.
Enablement & Transformation
Enable teams, build acceptance, provide ready-to-use GenAI solutions, and anchor AI in the organization for the long term.
How We Work
Modular, in clearly defined scopes. After each stage, you decide whether to continue.
Trusted By
Self-Assessment
How AI-Ready Is Your Company Really?
Take two minutes to find out where your company stands on AI.
Already know where you want to go?
The next section takes you straight to the right solution.
Already Looking for a Solution?
Three Questions to Find the Right Starting Point
Sovereign and local, flexible within the Microsoft ecosystem, or ready to use right away: which path suits you depends on regulation, priorities, and your team.
Our Technology Partners
The Right AI Technology for Your Use Case
Which AI solution is right for your project? That depends mainly on your data, your systems, and your goal. That’s why we stay vendor-neutral on purpose. We know our partners’ AI offerings from hands-on project work and work with you to find the combination that fits your company best.
Which AI Platform Fits Your Company?
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Key Partner
Matches your selectionsAI directly in Microsoft 365 apps, Teams, and Power Platform. Microsoft Entra ID permissions also apply to AI applications.
- A good fit if …
- Microsoft 365 and Azure are your established platforms and you want to bring AI to a large number of employees quickly.
- AI offerings
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- Microsoft Foundry (including Azure OpenAI)
- Microsoft 365 Copilot
- Copilot Studio
- Fabric
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Key Partner
Matches your selectionsGoogle’s own Gemini models with very large context windows for text, images, audio, and video. AI capabilities run directly in BigQuery.
- A good fit if …
- You want to analyze large volumes of unstructured data or use Google Workspace.
- AI offerings
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- Vertex AI
- Gemini Enterprise
- BigQuery
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Key Partner
Matches your selectionsAccess to many models through a single API, including Claude, Llama, Mistral, and Amazon Nova. Fine-grained security and network controls.
- A good fit if …
- Your workloads run on AWS and you want the flexibility to switch between or combine models.
- AI offerings
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- Amazon Bedrock
- Amazon Bedrock AgentCore
- Amazon SageMaker AI
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Key Partner
Matches your selectionsData, ML models, and AI agents on a single platform. Unity Catalog manages access and tracks lineage end to end.
- A good fit if …
- You’re building AI agents using enterprise data and want to bring data engineering and AI together.
- AI offerings
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- Mosaic AI
- Agent Bricks
- Unity Catalog
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Key Partner
Matches your selectionsLLM capabilities applied directly to your data through SQL. Your data stays within Snowflake’s security and governance perimeter.
- A good fit if …
- Your data is already in Snowflake and you want business departments to use AI without dedicated ML infrastructure.
- AI offerings
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- Snowflake Cortex AI
- Snowflake Intelligence
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b.telligent Network
Matches your selectionsA secure AI chat platform with access to multiple language models. Plus training and support from the first use case to a company-wide rollout.
- A good fit if …
- You want to roll out generative AI across your company and involve your employees from the start.
- AI offerings
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- Kambria AI platform
- Training programs
- AI enablement
Security and Governance
Production AI Needs Guardrails and a Path That Fits Your Regulatory Requirements
AI has to be controllable, secure, and usable in the long term. This is especially true in healthcare and pharma and in banking and finance: wherever confidential data, regulated processes, and automated decisions intersect. On top of that, the EU AI Act adds requirements for risk classification, transparency, and documentation. Governance doesn’t slow AI down. It makes it scalable.
Fig.: How an AI request passes through the guardrails, from the regulatory framework to human approval.
Which path fits depends on your data protection requirements, audit obligations, and the model performance you need.
Success Stories From Real Projects
Selected recent projects and the AI use cases behind them:
STAEDTLER
More than 40 use cases in twelve weeks and a clear platform decision for Microsoft Fabric.
Learn more
AENOVA
A new cloud data platform, clear roles, and nine Power BI dashboards in just ten months.
Learn more
idem
An LLM assistant answers fleet questions in natural language, directly from telematics data.
Learn more
Siemens Financial Services
More than 20 source systems moved to an AI-ready platform with Azure and Snowflake in 24 months.
Learn more
Nordex
AI image inspection in nacelle assembly: around one second per image, scalable across all plants.
Learn more
DKV Mobility
More than 30 use cases delivered across ten business units, breaking even in the first year.
Learn more
What Our Customers Value
Uli Pethe
Chief Information Officer at Aenova
Heiko Stranghöner
Head of Enterprise IT Solutions at STAEDTLER SE
Jens Zeller
Managing Director at idem telematics
Michael Bruckner
Head of Data Management & IT Program Mgr. SFS Data Platform at Siemens Financial Services
Stefan Tietze
Head of Technology Quality Management at Nordex Energy SE & Co. KG
Dr. Sönke Iwersen
Business Unit Lead Data & Intelligence & Analytics at DKV Mobility
Your Contacts for All Things AI
AI needs both strategy and the ability to deliver. Our team covers both.
Dr. Sebastian Petry
Domain Lead Data Science & AI
Dr. Michael Allgöwer
Management Consultant Data Science & AI
John Held
Management Consultant
Laura Weber
GenAI Solution Lead
Daniel Pöppelmann
GenAI Solution Lead
How We View AI
Dr. Sebastian Petry
Domain Lead Data Science & AI at b.telligent
Daniel Pöppelmann
GenAI Solution Lead at b.telligent
Frequently Asked Questions About AI Strategy & AI Consulting
Here Are the Most Important Answers
Getting Started and Approach
6 questions
What does AI consulting actually involve?
It helps you select, evaluate, implement, and operate AI use cases. At b.telligent, this covers AI strategy, preparing the data foundation, choosing the technology and operating model, building the solution, governance, and enabling your teams.
What sets b.telligent apart from other AI consultancies?
We come from data and BI. b.telligent has been building data platforms and BI landscapes since 2004, and the same DWH migrations we carried out manually for years now run with agent support. Strategy, implementation, and operations come from a single source, with no handover to another team.
How do we get started with AI the right way?
Don’t start with a tool. Start with where you are today. Clarify business goals, data foundation, potential risks, and relevant use cases. The AI maturity test works well for a self-assessment, and the 60-minute AI discovery call for a joint assessment.
How quickly will we see first results?
After the AI maturity test, you get an initial assessment online right away. In the AI discovery call, we go deeper into this assessment together in 60 minutes and derive prioritized next steps. How long it takes to reach the first production-ready result depends on the use case.
What happens after the AI discovery call?
Depending on your needs, the next step is a strategy workshop, a technical deep dive, or a clearly scoped implementation project. You get the assessment and next steps either way, whether or not you continue working with us.
How much does an AI project with b.telligent cost?
Getting started is free: the AI maturity test gives you an initial self-assessment online, and the AI discovery call is a no-obligation 60-minute conversation. After that, effort and cost depend on scope, the number of use cases, the state of your data foundation, and regulatory requirements. We work in clearly defined sub-scopes so you can decide again after each stage.
Prerequisites and Collaboration
5 questions
What data foundation do we need to use AI?
That depends on the use case. What matters is data that is available, accessible, and sufficiently reliable. For applications built on company knowledge, access rights, data quality, and the secure handling of confidential information also need to be clarified.
Do we need our own data science team?
No. To get started, you need one person who knows the processes and the data landscape, plus someone with access to the relevant systems. We work with your existing teams and transfer knowledge continuously throughout the project. If you want to build your own team, we support you with enablement and training.
Do we need an AI platform before we start?
No. Together we evaluate which platform and architecture decisions fit your initiative, from existing cloud offerings to self-hosted, sovereign solutions. An outdated data platform isn’t an obstacle either: in the AI discovery call, we clarify which use cases are already possible today and where modernization is a prerequisite. The migration often runs in parallel with the first AI use case.
Which industries does b.telligent support with AI?
Our focus areas are healthcare & pharma, banking and insurance, industry and manufacturing, retail and consumer goods, and mobility and energy. Reference projects on this page include STAEDTLER, Aenova, Siemens Financial Services, Nordex, and DKV Mobility.
Can AI help with migrating data platforms?
Yes. AI supports the analysis of legacy systems, the structuring of migration steps, and the modernization of data and reporting landscapes. A clear scope, quality assurance, and a step-by-step approach remain essential.
Security, Sovereignty, and Costs
5 questions
How can data protection and compliance be ensured when using AI?
With the right combination of governance, access concepts, guardrails, and technical security measures. For critical processes, approval steps by employees can be built in, in other words human-in-the-loop. This includes the EU AI Act’s requirements for risk classification, transparency, and documentation.
How do we prevent confidential data from reaching an AI model?
With a controlled intermediary layer between your applications and the model. It checks every request, detects and masks confidential content before the model is called, and logs what was processed. If your requirements don’t allow external processing, we run open-source models in your own environment.
Can we run AI without a US cloud provider?
Yes. There are three paths with different levels of sovereignty: commercial models via a controlled intermediary layer, European-hosted models in EU data centers, or open-source models in your own environment. Which path fits depends on your data protection requirements, audit obligations, and the model performance you need. More on running LLMs locally
How does b.telligent deal with errors and hallucinations in AI models?
With test concepts, benchmarking, and version control before going live, and human-in-the-loop for critical steps afterwards. This catches errors early, keeps quality traceable, and clearly assigns responsibility for AI-generated content from the start.
How do we avoid uncontrolled AI costs?
Transparency is the first step: we set up budgets and team structures in a controlled intermediary layer, make consumption visible per team and use case, and set limits. Because models remain interchangeable, you can switch to a cheaper model with the same functionality at any time, without rebuilding the application. We also evaluate architecture, scaling, and operations with a view to cost-effectiveness and long-term costs.
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AI Discovery Call
Ready for the Next Step?
In the free AI discovery call, we take 60 minutes to look at your situation: where your company stands today, which opportunities are realistic, and which next step makes sense.
Afterwards, you have an assessment and concrete next steps, whether or not you continue working with us.
What You Get
- Free 60-minute conversation
- No obligation or commitment to further services
- No preparation needed
Laura Weber
GenAI Solution Lead
Daniel Pöppelmann
GenAI Solution Lead
“We look forward to talking with you!”